Are you saying that you have not observed these things in the world? I definitely have. The blog didn't do the work for you, but if we look at some of the claims I think it is pretty clear:
a) increased training scale would result in highly fluent systems that would fool users into trusting untrustworthy output.
Can you possibly be claiming that this is not a common experience? Do you really need references to the legal cases which had hallucinated legal theories and citations? Or the utter slop being passed off as research papers?
b) large-scale AI would amplify bias in the source material.
The large investments nearly every frontier model development team spends on this problem is probably good enough evidence. Grok is another point of evidence. The studies showing that AI systems imitate gender bias in evaluating resumes is another. The gender bias in estimating names of people in sentences is another.
The blog actually mentions specific cases that exhibited all of these problems. They did not cite references for them, but you can use a search engine.
c) environment costs
This is widely discussed and documented. Take Xai's use of polluting turbine generators for their data center in for Collossus 2 in Mississippi as just a single example. Do you really need a reference for the environmental impact of the proposed data center in Utah that (as planned) will consume more energy than the entire state currently does?
d) training set audits are impossible.
Do you need substantiation of the inappropriate imagery in training data? The blog gives you a pretty solid reference.
... and so on ...
I suppose that it could be true that when you say "I don't see" you really meant "I didn't look at the blog". Is that why you can't see the substantiation?
alt Hacker News
Are you saying that you have not observed these things in the world? I definitely have. The blog didn't do the work for you, but if we look at some of the claims I think it is pretty clear:
a) increased training scale would result in highly fluent systems that would fool users into trusting untrustworthy output.
Can you possibly be claiming that this is not a common experience? Do you really need references to the legal cases which had hallucinated legal theories and citations? Or the utter slop being passed off as research papers?
b) large-scale AI would amplify bias in the source material.
The large investments nearly every frontier model development team spends on this problem is probably good enough evidence. Grok is another point of evidence. The studies showing that AI systems imitate gender bias in evaluating resumes is another. The gender bias in estimating names of people in sentences is another.
The blog actually mentions specific cases that exhibited all of these problems. They did not cite references for them, but you can use a search engine.
c) environment costs
This is widely discussed and documented. Take Xai's use of polluting turbine generators for their data center in for Collossus 2 in Mississippi as just a single example. Do you really need a reference for the environmental impact of the proposed data center in Utah that (as planned) will consume more energy than the entire state currently does?
d) training set audits are impossible.
Do you need substantiation of the inappropriate imagery in training data? The blog gives you a pretty solid reference.
... and so on ...
I suppose that it could be true that when you say "I don't see" you really meant "I didn't look at the blog". Is that why you can't see the substantiation?